Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3D information and coronal plane information with great diagnostic value. However, manually screening and diagnosing tumors from ABUS images is very time-consuming and overlooks of abnormalities may happen. In this study, we propose a novel two-stage 3D detection network for locating suspected lesion areas and further classifying lesions as benign or malignant tumors. Specifically, we propose a 3D detection network rather than frequently-used segmentation network to locate lesions in ABUS images, thus our network can make full use of the spatial context information in ABUS images. A novel similarity loss is designed to effectively distinguish lesions from background. Then a classification network is employed to identify the located lesions as benign or malignant. An IoU-balanced classification loss is adopted to improve the correlation between classification and localization task. The efficacy of our network is verified from a collected dataset of 418 patients with 145 benign tumors and 273 malignant tumors. Experiments show our network attains a sensitivity of 97.66% with 1.23 false positives (FPs), and has an area under the curve(AUC) value of 0.8720.
Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. Although deep learning-based approaches have been proposed and achieved promising results, existing approaches have difficulty in detecting small breast tumors. The capacity to detecting small tumors is particularly important in finding early stage cancers using computer-aided diagnosis (CAD) systems. In this paper, we propose a novel deep learning architecture called Small Tumor-Aware Network (STAN), to improve the performance of segmenting tumors with different size. The…
The Chinese University of Hong Kong（香港中文大学）；University of Cambridge（剑桥大学）；Inception Institute of Artificial Intelligence（启迪人工智能研究院）；The Hong Kong University of Science and Technology（香港科技大学）；Jinan University（暨南大学）；Southern University of Science and Technology（南方科技大学）；Chinese Academy of Sciences（中国科学院）
Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limit…
University of Idaho（爱达荷大学）；University of Utah School of Medicine（犹他大学医学院）
Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging, due to the speckle noise, varying tumor shapes and sizes among patients, and the existence of tumor-like image regions. Recently, deep learning-based approaches have achieved great success for biomedical image analysis, but current state-of-the-art approaches achieve poor performance for segmenting …
School of Computing, University of Buckingham（白金汉大学计算学院）
Deep Convolutional Neural Networks (CNN) provides an "end-to-end" solution for image pattern recognition with impressive performance in many areas of application including medical imaging. Most CNN models of high performance use hand-crafted network architectures that require expertise in CNNs to utilise their potentials. In this paper, we applied the Efficient Neural Architecture Search (ENAS) method to find optimal CNN architectures for classifying breast lesions from ultrasound (US) images. Our empirical study with a dataset of 524 US images shows that the optimal models generated by using …
University of Minnesota（明尼苏达大学）；University of Cincinnati（辛辛那提大学）
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis base…
Tunghai University（东海大学）；Changhua Christian Hospital（彰化基督教医院）
Malignant and benign breast tumors present differently in their shape and size on sonography. Morphological information provided by tumor contours are important in clinical diagnosis. However, ultrasound images contain noises and tissue texture; clinical diagnosis thus highly depends on the experience of physicians. The manual way to sketch three-dimensional (3D) contours of breast tumor is a time-consuming and complicate task. If automatic contouring could provide a precise breast tumor contour that might assist physicians in making an accurate diagnosis. This study presents an efficient meth…
Progressive Dual Priori Network for Generalized Breast Tumor Segmentation
Guizhou University（贵州大学）；Harbin Medical University Cancer Hospital（哈尔滨医科大学附属肿瘤医院）；Univ Lyon（里昂大学）；INSA Lyon（里昂国立应用科学学院）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CREATIS（CREATIS研究所）
Breast Ultrasound Report Generation using LangChain
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Chung-Ang University Hospital（中央大学医院）；Chung-Ang University College of Medicine（中央大学医学院）
University of Malaya（马来亚大学）；University of Tehran（德黑兰大学）
Reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low cost method for early diagnosis of breast cancer. The accuracy of the diagnosis is however highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The leverage in deep convolutional neural network approaches provided solutions in efficient analysis of breast ultrasound images. In this study, we proposed a new framework for classification of breast cancer lesions by use of an attention module in modified VGG16 architect…
Xiamen University（厦门大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究院）；The Hong Kong Polytechnic University（香港理工大学）
Breast lesion detection in ultrasound is critical for breast cancer diagnosis. Existing methods mainly rely on individual 2D ultrasound images or combine unlabeled video and labeled 2D images to train models for breast lesion detection. In this paper, we first collect and annotate an ultrasound video dataset (188 videos) for breast lesion detection. Moreover, we propose a clip-level and video-level feature aggregated network (CVA-Net) for addressing breast lesion detection in ultrasound videos by aggregating video-level lesion classification features and clip-level temporal features. The clip-…
Universidad Técnica Particular de Loja（洛哈技术大学）；Universitat Politécnica de Valencia（瓦伦西亚理工大学）；University of Waterloo（滑铁卢大学）
Breast cancer is the most common invasive cancer in women, and the second main cause of death. Breast cancer screening is an efficient method to detect indeterminate breast lesions early. The common approaches of screening for women are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload by pathologists, who are prone to diagnostic errors. Thus, the aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learn…
Stony Brook University（石溪大学）；Stony Brook Medicine（石溪医学院）；Stony Brook University Hospital（石溪大学医院）；University of Michigan Medical School（密歇根大学医学院）；National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；Emory University（埃默里大学）
Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network (CNN) analysis pipelines to generate combined maps of cancer regions and tumor infiltrating lymphocytes (TILs) in routine diagnostic breast cancer whole slide tissue images (WSIs). We produce interactive whole slide maps that provide 1) insight about the structural patterns and spatial distribution of lymphocytic infiltrates and 2) facilitate improved quantification of TILs. We eva…
Graduate School at Shenzhen, Tsinghua University（清华大学深圳研究生院）；Shenzhen City Baoan District Women’s and Children’s Hospital（深圳市宝安区妇幼保健院）；Shenzhen Hospital of Peking University（北京大学深圳医院）；Beijing Tsinghua Changgung Hospital, Tsinghua University（清华大学北京清华长庚医院）；School of Biomedical Informatics, University of Texas Health Sciences Center at Houston（德克萨斯大学休斯顿健康科学中心生物医学信息学院）
Background: Ultrasound is one of the preferred choices for early screening of dense breast cancer. Clinically, doctors have to manually write the screening report which is time-consuming and laborious, and it is easy to miss and miswrite. Aim: We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing. Methods: AI was used to efficiently generate personalized breast ultrasound screening preliminary reports, especially for…
UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis
School of Engineering, Tokyo Institute of Technology（东京技术大学工学院）；Faculty of Information Technology Nguyen Tat Thanh University（Nguyen Tat Thanh大学信息科技学院）
Korea University（高丽大学）；Hankik University of Foreign Studies（韩国外国语大学）
We propose a method for effectively utilizing weakly annotated image data in an object detection tasks of breast ultrasound images. Given the problem setting where a small, strongly annotated dataset and a large, weakly annotated dataset with no bounding box information are available, training an object detection model becomes a non-trivial problem. We suggest a controlled weight for handling the effect of weakly annotated images in a two stage object detection model. We~also present a subsequent active learning scheme for safely assigning weakly annotated images a strong annotation using the …
Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；University of California, San Diego（加州大学圣迭戈分校）
Morphological features play an important role in breast mass classification in sonography. While benign breast masses tend to have a well-defined ellipsoidal contour, shape of malignant breast masses is commonly ill-defined and highly variable. Various handcrafted morphological features have been developed over the years to assess this phenomenon and help the radiologists differentiate benign and malignant masses. In this paper we propose an automatic approach to morphology analysis, we express shapes of breast masses as points on the Kendall's shape manifold. Next, we use the full Procrustes …
Nankai University（南开大学）；Civil Aviation General Hospital（民航总医院）；Chinese PLA General Hospital（中国人民解放军总医院）
Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention. However, accurate segmentation of malignant breast lesions is a challenging task due to the effects of heterogeneous structure and similar intensity distributions. In this paper, a novel bidirectional aware guidance network (BAGNet) is proposed to segment the malignant lesion from breast ultrasound images. Specifically, the bidirectional aware guidance network is used to capture the context between global (low-level) and local (high-level) features from the input coarse sal…
Northeastern University（东北大学）；Sino-Dutch Biomedical and Information Engineering School（中荷生物医学与信息工程学院）
Ultrasound image diagnosis of breast tumors has been widely used in recent years. However, there are some problems of it, for instance, poor quality, intense noise and uneven echo distribution, which has created a huge obstacle to diagnosis. To overcome these problems, we propose a novel method, a breast cancer classification with ultrasound images based on SLIC (BCCUI). We first utilize the Region of Interest (ROI) extraction based on Simple Linear Iterative Clustering (SLIC) algorithm and region growing algorithm to extract the ROI at the super-pixel level. Next, the features of ROI are extr…
Breast cancer is the most common invasive cancer in women. Besides the primary B-mode ultrasound screening, sonographers have explored the inclusion of Doppler, strain and shear-wave elasticity imaging to advance the diagnosis. However, recognizing useful patterns in all types of images and weighing up the significance of each modality can elude less-experienced clinicians. In this paper, we explore, for the first time, an automatic way to combine the four types of ultrasonography to discriminate between benign and malignant breast nodules. A novel multimodal network is proposed, along with pr…
East Delta University（东三角洲大学）；University of Chittagong（吉大港大学）
Breast cancer has become a symbol of tremendous concern in the modern world, as it is one of the major causes of cancer mortality worldwide. In this regard, breast ultrasonography images are frequently utilized by doctors to diagnose breast cancer at an early stage. However, the complex artifacts and heavily noised breast ultrasonography images make diagnosis a great challenge. Furthermore, the ever-increasing number of patients being screened for breast cancer necessitates the use of automated end-to-end technology for highly accurate diagnosis at a low cost and in a short time. In this conce…
Uncertainty-Gated Deformable Network for Breast Tumor Segmentation in MR Images
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Information Engineering, Hefei University of Science（合肥科学技术大学计算机科学与信息工程学院）；Department of Radiology, Southwest Hospital, Army Medical University（西南医院放射科，中国人民解放军陆军军医大学）；School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）
Neuromation OU；University of Oulu（奥卢大学）；MIT（麻省理工学院）；University of Michigan（密歇根大学）；Steklov Mathematical Institute（斯捷克洛夫数学研究所）
Breast cancer is one of the main causes of death worldwide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor's response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patient's survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learning-based approaches for automatic assessment…
University of Warwick（华威大学）；Queen Mary University of London（伦敦玛丽女王大学）；University of Sheffield（谢菲尔德大学）
The quantification of tumor-infiltrating lymphocytes (TILs) has been shown to be an independent predictor for prognosis of breast cancer patients. Typically, pathologists give an estimate of the proportion of the stromal region that contains TILs to obtain a TILs score. The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge, aims to assess the prognostic significance of computer-generated TILs scores for predicting survival as part of a Cox proportional hazards model. For this challenge, as the TIAger team, we have developed an algorithm to first segment tumor vs. stroma, before…
Illinois Institute of Technology（伊利诺伊理工学院）；Rutgers University（罗格斯大学）
Automatically segmenting lesion area in breast ultrasound (BUS) images is a challenging one due to its noise, speckle and artifacts. Edge-map of BUS images also does not help because in most cases the edge-map gives no information whatsoever. Almost all segmentation technique takes the edge-map of the image as its first step, though there are a few algorithms that try to avoid edge-maps as well. Improving the edge-map of breast ultrasound images theoretically improves the chances of automatic segmentation to be more precise. In this paper, we propose a semi-automatic technique of histogram spl…
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classif…
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images
Florida Atlantic University（佛罗里达大西洋大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Missouri University of Science and Technology（密苏里科技大学）；Florida International University（佛罗里达国际大学）
The advancements in deep learning technologies have produced immense contributions to biomedical image analysis applications. With breast cancer being the common deadliest disease among women, early detection is the key means to improve survivability. Medical imaging like ultrasound presents an excellent visual representation of the functioning of the organs; however, for any radiologist analysing such scans is challenging and time consuming which delays the diagnosis process. Although various deep learning based approaches are proposed that achieved promising results, the present article intr…
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM)（巴塞罗那人工智能医学实验室）；Departament de Matemàtiques i Informàtica, Universitat de Barcelona（巴塞罗那大学数学与计算机科学系）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（生物医学成像机器学习研究所，海德堡慕尼黑）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Department of Oncology-Pathology, Karolinska Institutet（卡罗林斯卡研究所肿瘤-病理学系）；School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）
Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound
Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University（深圳大学医学部生物医学工程学院）；Shenzhen University（深圳大学）
College of Information Science and Technology, Northwest University（西北大学信息科学与技术学院）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Breast ultrasound (US) is an effective imaging modality for breast cancer detec-tion and diagnosis. The structural characteristics of breast lesion play an im-portant role in Computer-Aided Diagnosis (CAD). In this paper, a novel struc-ture-aware triplet path networks (SATPN) was designed to integrate classifica-tion and two image reconstruction tasks to achieve accurate diagnosis on US im-ages with small training dataset. Specifically, we enhance clinically-approved breast lesion structure characteristics though converting original breast US imag-es to BIRADS-oriented feature maps (BFMs) with…
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations
Department of Health Research（健康研究系）；SINTEF Digital（SINTEF数字）；Department of Physics（物理系）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；Department of Circulation and Medical Imaging（循环与医学成像系）；St. Olavs hospital（圣奥拉夫医院）；Department of Neuromedicine and Movement Science（神经医学与运动科学系）
Shiraz University（设拉子大学）；Koc University（科奇大学）；Shiraz University of Medical Sciences（设拉子医科大学）
The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting tumor progression and its treatment response. The value of the Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC), known as the most common cancer in women worldwide, has been highlighted in the literature. Due to the indeterminable and subjective nature of Ki-67 as well as TILs scoring, automated methods using machine learning, specifically approaches based on deep learning, have attracted attention. Yet, deep learning methods need consi…
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；National University of Singapore（新加坡国立大学）；Shenzhen University（深圳大学）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Helmholtz Imaging（亥姆霍兹成像机构）
Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise r…
School of Electrical Engineering, Tel-Aviv University（特拉维夫大学电气工程学院）；The Manuel and Raquel Klachky Chair of Image Processing, School of Electrical Engineering, Tel-Aviv University（特拉维夫大学电气工程学院 Manuel and Raquel Klachky 图像处理教授职位）；Diagnostic Imaging, Sheba Medical Center, affiliated to the Sackler School of Medicine, Tel-Aviv University（舍巴医疗中心诊断影像科，隶属于特拉维夫大学萨克勒医学院）
Mammography and ultrasound are extensively used by radiologists as complementary modalities to achieve better performance in breast cancer diagnosis. However, existing computer-aided diagnosis (CAD) systems for the breast are generally based on a single modality. In this work, we propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images. We present various approaches and show a consistent improvement in performance when utilizing both modalities. The proposed approach is based on a GoogleNet architecture, fine-tuned for o…
Charles Stark Draper Laboratory（查尔斯·斯塔克·德拉普尔实验室）；Stanford Medicine（斯坦福医学）；Bracco Diagnostics Corporation（布拉科诊断公司）
Molecularly targeted contrast enhanced ultrasound (mCEUS) is a clinically promising approach for early cancer detection through targeted imaging of VEGFR2 (KDR) receptors. We have developed computational enhancement techniques for mCEUS tailored to address the unique challenges of imaging contrast accumulation in humans. These techniques utilize dynamic analysis to distinguish molecularly bound contrast agent from other contrast-mode signal sources, enabling analysis of contrast agent accumulation to be performed during contrast bolus arrival when the signal due to molecular binding is stronge…
Politecnico di Milano（米兰理工大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；Università Politecnica delle Marche（马尔凯理工大学）
Breast cancer is one of the deadliest cancer worldwide. Timely detection could reduce mortality rates. In the clinical routine, classifying benign and malignant tumors from ultrasound (US) imaging is a crucial but challenging task. An automated method, which can deal with the variability of data is therefore needed. In this paper, we compared different Convolutional Neural Networks (CNNs) and transfer learning methods for the task of automated breast tumor classification. The architectures investigated in this study were VGG-16 and Inception V3. Two different training strategies were investiga…
Computer-aided detection (CAD) of benign and malignant breast lesions becomes increasingly essential in breast ultrasound (US) imaging. The CAD systems rely on imaging features identified by the medical experts for their performance, whereas deep learning (DL) methods automatically extract features from the data. The challenge of the DL is the insufficiency of breast US images available to train the DL models. Here, we present an ensemble transfer learning model to classify benign and malignant breast tumors using B-mode breast US (B-US) and strain elastography breast US (SE-US) images. This m…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mayo Clinic（梅奥诊所）；Washington University in St. Louis（圣路易斯华盛顿大学）；Beckman Institute, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校贝克曼研究所）
Automatic breast lesion detection and classification is an important task in computer-aided diagnosis, in which breast ultrasound (BUS) imaging is a common and frequently used screening tool. Recently, a number of deep learning-based methods have been proposed for joint localization and classification of breast lesions using BUS images. In these methods, features extracted by a shared network trunk are appended by two independent network branches to achieve classification and localization. Improper information sharing might cause conflicts in feature optimization in the two branches and leads …
Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment
Viterbi School of Engineering（维特比工程学院）；College of Engineering（工程学院）；University of Southern California（南加州大学）；Rochester Institute of Technology（罗切斯特理工学院）；Northeastern University（东北大学）；School of Arts & Sciences（艺术与科学学院）；State University of New York Plattsburgh（纽约州立大学普拉茨堡分校）
